Frequent Cognitive Tests Increase Power for Alzheimer’s Disease Clinical Trials
Bibliographic record
Abstract
Abstract Background Many clinical trials use performance‐based cognitive or functional test results as primary outcome measures. As few as two measurements, typically pre‐ and post‐intervention, can be used to define a cognitive trajectory. Increasing the number and frequency of assessments can increase power to test the primary hypothesis. However, increased assessment frequency may be expensive and burdensome to participants and staff. Frequent neuropsychological testing may also introduce a practice effect or learning effect. Trials should be designed with attention to the costs and benefits of more frequent assessments. Method We conducted two clinical trials and performed Monte Carlo simulations. The Montreal Cognitive Assessment (MoCA) and the MCI Screen (MCIS) were used to assess cognition; the Functional Assessment Staging Tool (FAST) was used to assess function. In addition to simulated data, we employed data from the Coaching for Cognition in Alzheimer’s (COCOA) and Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trials. Result Effect sizes and measurement variability were similar between the two cognitive screens. More frequent assessments increased power to reject a null hypothesis The major factor driving this improved power was the influence of data from individuals withdrawing from or dropping out of the trial prior to trial end. More frequent testing captured more of the effect of the intervention over time in those individuals, and therefore, increased power for the entire study. The design of the MCIS with rotating word lists facilitated frequent testing without a learning effect. Hypothetically, a more frequently administered assessment might have less precision than a different assessment administered less frequently, and the unit costs of these assessments might differ. Under a wide range of parameters modeled in simulations, the advantages of more frequent testing outweighed decreases in the precision of individual assessments. Conclusion More frequent testing should be preferred in clinical trials. Use of shorter and/or remote tests may facilitate such frequency, reduce costs, and increase safety during pandemics. In addition to gains in statistical power, dense measurements have other benefits. They may also reveal non‐linear trajectories and better enable longitudinal analyses such as time dependencies and dose‐response relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.405 | 0.661 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".